4.7 Article

BRICK v0.2, a simple, accessible, and transparent model framework for climate and regional sea-level projections

期刊

GEOSCIENTIFIC MODEL DEVELOPMENT
卷 10, 期 7, 页码 2741-2760

出版社

COPERNICUS GESELLSCHAFT MBH
DOI: 10.5194/gmd-10-2741-2017

关键词

-

资金

  1. National Science Foundation through the Network for Sustainable Climate Risk Management (SCRiM) under NSF [GEO-1240507]
  2. Penn State Center for Climate Risk Management
  3. Directorate For Geosciences [1240507] Funding Source: National Science Foundation

向作者/读者索取更多资源

Simple models can play pivotal roles in the quantification and framing of uncertainties surrounding climate change and sea-level rise. They are computationally efficient, transparent, and easy to reproduce. These qualities also make simple models useful for the characterization of risk. Simple model codes are increasingly distributed as open source, as well as actively shared and guided. Alas, computer codes used in the geoscience scan often be hard to access, run, modify (e.g., with regards to assumptions and model components), and review. Here, we describe the simple model framework BRICK (Building blocks for Relevant Ice and Climate Knowledge) v0.2 and its underlying design principles. The paper adds detail to an earlier published model setup and discusses the inclusion of a land water storage component. The framework largely builds on existing models and allows for projections of global mean temperature as well as regional sea levels and coastal flood risk. BRICK is written in R and Fortran. BRICK gives special attention to the model values of transparency, accessibility, and flexibility in order to mitigate the above-mentioned issues while maintaining a high degree of computational efficiency. We demonstrate the flexibility of this framework through simple model intercomparison experiments. Furthermore, we demonstrate that BRICK is suitable for risk assessment applications by using a didactic example in local flood risk management.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据